Visually summarizing the Evolution of Documents under a Social Tag

Tags are intensively used in social platforms to annotate resources: Tagging is a social phenomenon, because users do not only annotate to organize their resources but also to associate semantics to resources contributed by third parties. This leads often to semantic ambiguities: Popular tags are associated with very disparate meanings, even to the extend that some tags (e.g. ”beautiful” or ”toread”) are irrelevant to the semantics of the resources they annotate. We propose a method that learns a topic model for documents under a tag and visualizes the different meanings associated

[1]  Laks V. S. Lakshmanan,et al.  Discovering leaders from community actions , 2008, CIKM '08.

[2]  Myra Spiliopoulou,et al.  Topic Evolution in a Stream of Documents , 2009, SDM.

[3]  ChengXiang Zhai,et al.  Automatic labeling of multinomial topic models , 2007, KDD '07.

[4]  Issei Fujishiro,et al.  The elements of graphing data , 2005, The Visual Computer.

[5]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[6]  B. S. Manjunath,et al.  Not all tags are created equal: Learning flickr tag semantics for global annotation , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[7]  Sudipto Guha,et al.  Clustering Data Streams: Theory and Practice , 2003, IEEE Trans. Knowl. Data Eng..

[8]  John D. Lafferty,et al.  Dynamic topic models , 2006, ICML.

[9]  Thomas Hofmann,et al.  Unsupervised Learning by Probabilistic Latent Semantic Analysis , 2004, Machine Learning.

[10]  Andrew McCallum,et al.  Topics over time: a non-Markov continuous-time model of topical trends , 2006, KDD '06.

[11]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[12]  ChengXiang Zhai,et al.  Discovering evolutionary theme patterns from text: an exploration of temporal text mining , 2005, KDD '05.

[13]  Chong Wang,et al.  Reading Tea Leaves: How Humans Interpret Topic Models , 2009, NIPS.

[14]  Lucy T. Nowell,et al.  ThemeRiver: Visualizing Thematic Changes in Large Document Collections , 2002, IEEE Trans. Vis. Comput. Graph..

[15]  Daniel Barbará,et al.  On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[16]  Meng Chang Chen,et al.  Using Incremental PLSI for Threshold-Resilient Online Event Analysis , 2008, IEEE Transactions on Knowledge and Data Engineering.